Generally, principal component analysis (PCA) is an approach for dimensionality reduction of high dimensional data by extracting relevant components from the high dimensional data. The data with reduced dimensions may be easier for analysis. Typically, PCA has wide applications in various fields such as computer vision, neuroscience, social science, computational finance, etc. For example, in applications used in computational finance such as haircut computation, PCA computation operations may have to be performed frequently all throughout the day. Further, big investment banks have many client portfolios that mandate simultaneous PCA computation for all the client portfolios. The execution time for a single PCA computation increases with the size of the portfolio. However, if there are multiple client portfolios, several such PCA computations may have to be done concurrently.
The inventors here have recognized several technical problems with such conventional systems, as explained below. PCA computation require processing of input data in the form of matrices and may involve tridiagnalization of the input matrix. There are few conventional PCA based computing systems that perform sequential tridiagonalization methods, however, such system implementations are not sufficiently time effective in performing large scale data computations. Such computations are computationally expensive and take a long time for being handled by the central processing unit (CPU) of a computing system for handling PCA related requests.